Mining Health Social Media with Sentiment Analysis
Journal
Journal of Medical Systems
Journal Volume
40
Journal Issue
11
Date Issued
2016
Author(s)
Abstract
With the rapid development of the Internet, more and more users utilize health communities (known as forums) to find health-related information, share their medical stories and experiences, or interact with other people in the communities. In this paper, we propose a framework to analyze the user-generated contents in a health community. The proposed framework contains three phases. First, we extract medical terms, including conditions, symptoms, treatments, effectiveness and side effects to form a virtual document for each question in the community. Next, we modify Latent Dirichlet Allocation (LDA) by adding a weighted scheme, called conLDA, to cluster virtual documents with similar medical term distributions into a conditional topic (C-topic). Finally, we analyze the clustered C-topics by sentiment polarities, and physiological and psychological sentiment. The experiment results show that conLDA outperforms the original LDA, and can cluster relevant medical terms and relevant questions together. The C-topics clustered by conLDA are more thematic than those clustered by the original LDA. The results of sentiment analysis may provide a quick reference and valuable insights for patients, caregivers and doctors. ? 2016, Springer Science+Business Media New York.
Subjects
Health social media
Latent Dirichlet Allocation
Sentiment analysis
SDGs
Other Subjects
caregiver; experimental model; extract; human; human experiment; mining; social media; symptom; consumer health information; data mining; procedures; social media; statistics and numerical data; Consumer Health Information; Data Mining; Humans; Social Media
Type
journal article